![[2401.10846] Distributed Genetic Algorithm for Feature Selection](https://ar5iv.labs.arxiv.org/html/2401.10846/assets/images/ga_diagram.png)
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%20are%20increasingly%20being%20used%20to%20perform%20algorithm-selection%20in%20combinatorial%20optimisation%20domains%2C%20particularly%20as%20they%20accommodate%20input%20representations%20which%20avoid%20designing%20and%20calculating%20features.%20Mounting%20evidence%20from%20domains%20that%20use%20images%20as%20input%20shows%20that%20deep%20convolutional%20networks%20are%20vulnerable%20to%20adversarial%20samples%2C%20in%20which%20a%20small%20perturbation%20of%20an%20instance%20can%20cause%20the%20DNN%20to%20misclassify.%20However%2C%20it%20remains%20unknown%20as%20to%20whether%20deep%20recurrent%20networks%20(DRN)%20which%20have%20recently%20been%20shown%20promise%20as%20algorithm-selectors%20in%20the%20bin-packing%20domain%20are%20equally%20vulnerable.%20We%20use%20an%20evolutionary%20algorithm%20(EA)%20to%20find%20perturbations%20of%20instances%20from%20two%20existing%20benchmarks%20for%20online%20bin%20packing%20that%20cause%20trained%20DRNs%20to%20misclassify:%20adversarial%20samples%20are%20successfully%20generated%20from%20up%20to%2056%25%20of%20the%20original%20instances%20depending%20on%20the%20dataset.%20Analysis%20of%20the%20new%20misclassified%20instances%20sheds%20light%20on%20the%20%60fragility'%20of%20some%20training%20instances%2C%20i.e.%20instances%20where%20it%20is%20trivial%20to%20find%20a%20small%20perturbation%20that%20results%20in%20a%20misclassification%20and%20the%20factors%20that%20influence%20this.%20Finally%2C%20the%20method%20generates%20a%20large%20number%20of%20new%20instances%20misclassified%20with%20a%20wide%20variation%20in%20confidence%2C%20providing%20a%20rich%20new%20source%20of%20training%20data%20to%20create%20more%20robust%20models.)
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